ORSSO-DETR: Small Object Detection Model for Optical Remote Sensing Images Based on an Improved Efficient Encoder

Optical remote sensing image object detection is widely applied in industrial monitoring, infrastructure maintenance, and intelligent scheduling, yet its accuracy is limited by complex background interference, dense multi-scale object distributions, and loss of small object details. To address these challenges, this paper proposes the ORSSO-DETR model based on RT-DETR, which improves the hybrid encoder’s ability to adapt to the characteristics of optical remote sensing images. The proposed method integrates the C3K2 multi-scale fusion module with the FCM feature complementary mapping module to enhance feature fusion, introduces a dynamic convolution-based DynamicConv downsampling module and the EUCB upsampling technique to improve multi-scale feature retention and texture detail preservation, optimizes the encoder structure by preprocessing high-level features with 1 × 1 convolution, and replaces the original normalization layer in the Transformer with the Dynamic Tanh module to strengthen nonlinear expressive capability. Experimental results show that the proposed method achieves 96.2% mAP@50 and 64.4% mAP@50:95 on the NWPU VHR-10 dataset, improving by 3.9% and 2.9% over the baseline, respectively. On the RSOD dataset, it achieves 97.6% mAP@50 and 71.2% mAP@50:95, with improvements of 2.5% and 2.8%, respectively. These results validate the effectiveness of the proposed method in improving small object detection accuracy in optical remote sensing images.